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Aggregation Network

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Jie Zhou – One of the best experts on this subject based on the ideXlab platform.

  • Learning Discriminative Aggregation Network for Video-Based Face Recognition and Person Re-identification
    International Journal of Computer Vision, 2018
    Co-Authors: Yongming Rao, Jie Zhou

    Abstract:

    In this paper, we propose a discriminative Aggregation Network method for video-based face recognition and person re-identification, which aims to integrate information from video frames for feature representation effectively and efficiently. Unlike existing video Aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn an Aggregation Network to generate more discriminative images compared to the raw input frames. Our framework reduces the number of image frames per video to be processed and significantly speeds up the recognition procedure. Furthermore, low-quality frames containing misleading information can be well filtered and denoised during the Aggregation procedure, which makes our method more robust and discriminative. Experimental results on several widely used datasets show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and the accuracy for video-based face recognition and person re-identification.

  • ICCV – Learning Discriminative Aggregation Network for Video-Based Face Recognition
    2017 IEEE International Conference on Computer Vision (ICCV), 2017
    Co-Authors: Yongming Rao, Ji Lin, Jie Zhou

    Abstract:

    In this paper, we propose a discriminative Aggregation Network (DAN) method for video face recognition, which aims to integrate information from video frames effectively and efficiently. Unlike existing Aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn an Aggregation Network that produces more discriminative synthesized images compared to raw input frames. Our framework reduces the number of frames to be processed and significantly speed up the recognition procedure. Furthermore, low-quality frames containing misleading information are filtered and denoised during the Aggregation process, which makes our system more robust and discriminative. Experimental results show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and accuracy on three widely used datasets.

Gang Hua – One of the best experts on this subject based on the ideXlab platform.

  • gated context Aggregation Network for image dehazing and deraining
    Workshop on Applications of Computer Vision, 2019
    Co-Authors: Dongdong Chen, Qingnan Fan, Jing Liao, Liheng Zhang, Dongdong Hou, Lu Yuan, Gang Hua

    Abstract:

    Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context Aggregation Network to directly restore the final haze-free image. In this Network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-Network to fuse the features from different levels. Extensive experiments demonstrate that our method can surpass previous state-of-the-art methods by a large margin both quantitatively and qualitatively. In addition, to demonstrate the generality of the proposed method, we further apply it to the image deraining task, which also achieves the state-of-the-art performance.

  • WACV – Gated Context Aggregation Network for Image Dehazing and Deraining
    2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019
    Co-Authors: Dongdong Chen, Qingnan Fan, Jing Liao, Liheng Zhang, Dongdong Hou, Lu Yuan, Gang Hua

    Abstract:

    Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context Aggregation Network to directly restore the final haze-free image. In this Network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-Network to fuse the features from different levels. Extensive experiments demonstrate that our method can surpass previous state-of-the-art methods by a large margin both quantitatively and qualitatively. In addition, to demonstrate the generality of the proposed method, we further apply it to the image deraining task, which also achieves the state-of-the-art performance.

  • Gated Context Aggregation Network for Image Dehazing and Deraining
    arXiv: Computer Vision and Pattern Recognition, 2018
    Co-Authors: Dongdong Chen, Qingnan Fan, Jing Liao, Liheng Zhang, Dongdong Hou, Lu Yuan, Gang Hua

    Abstract:

    Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context Aggregation Network to directly restore the final haze-free image. In this Network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-Network to fuse the features from different levels. Extensive experiments demonstrate that our method can surpass previous state-of-the-art methods by a large margin both quantitatively and qualitatively. In addition, to demonstrate the generality of the proposed method, we further apply it to the image deraining task, which also achieves the state-of-the-art performance. Code has been made available at this https URL.

Yongming Rao – One of the best experts on this subject based on the ideXlab platform.

  • Learning Discriminative Aggregation Network for Video-Based Face Recognition and Person Re-identification
    International Journal of Computer Vision, 2018
    Co-Authors: Yongming Rao, Jie Zhou

    Abstract:

    In this paper, we propose a discriminative Aggregation Network method for video-based face recognition and person re-identification, which aims to integrate information from video frames for feature representation effectively and efficiently. Unlike existing video Aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn an Aggregation Network to generate more discriminative images compared to the raw input frames. Our framework reduces the number of image frames per video to be processed and significantly speeds up the recognition procedure. Furthermore, low-quality frames containing misleading information can be well filtered and denoised during the Aggregation procedure, which makes our method more robust and discriminative. Experimental results on several widely used datasets show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and the accuracy for video-based face recognition and person re-identification.

  • ICCV – Learning Discriminative Aggregation Network for Video-Based Face Recognition
    2017 IEEE International Conference on Computer Vision (ICCV), 2017
    Co-Authors: Yongming Rao, Ji Lin, Jie Zhou

    Abstract:

    In this paper, we propose a discriminative Aggregation Network (DAN) method for video face recognition, which aims to integrate information from video frames effectively and efficiently. Unlike existing Aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn an Aggregation Network that produces more discriminative synthesized images compared to raw input frames. Our framework reduces the number of frames to be processed and significantly speed up the recognition procedure. Furthermore, low-quality frames containing misleading information are filtered and denoised during the Aggregation process, which makes our system more robust and discriminative. Experimental results show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and accuracy on three widely used datasets.